syndicalt/zaxy

Overview

Zaxy provides a durable, auditable memory system for AI agents using a hash-chained Eventloom log as the source of truth. It includes an embedded temporal knowledge graph for local-first reasoning, optional Neo4j or Postgres, and cited Memory Checkout for compact context. Model-facing MCP tools enable retrieval, capture, and feedback.

Best for

Best for
Developers building agent systems that need durable, auditable memory with local-first reasoning

Use cases

• Log agent actions in an immutable, tamper-evident event chain
• Query a temporal knowledge graph to support agent reasoning and decision-making
• Retrieve compact, cited context for LLM prompts via Memory Checkout

Notes

Zaxy provides a durable, auditable memory system for AI agents using a hash-chained Eventloom log as the source of truth. It includes an embedded temporal knowledge graph for local-first reasoning, optional Neo4j or Postgres, and cited Memory Checkout for compact context. Model-facing MCP tools enable retrieval, capture, and feedback.

10 stars on GitHub. Last updated 2026-06-15. Licensed MIT.

Use cases

• Log agent actions in an immutable, tamper-evident event chain
• Query a temporal knowledge graph to support agent reasoning and decision-making
• Retrieve compact, cited context for LLM prompts via Memory Checkout

Pros

• Hash-chained log ensures audibility and integrity of agent memory
• Local-first temporal knowledge graph eliminates need for external databases by default
• Cited Memory Checkout reduces context footprint for efficient LLM calls

Cons

• Requires Python environment and MCP protocol integration
• Project is early-stage with limited community adoption (10 stars)
• Optional external database setup (Neo4j/Postgres) may add complexity for advanced use

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.